A Detailed iDAFNO architecture Similar to the eDAFNO architecture shown in (6)

The dataset is obtained from Li et al. (2022a), which consists of an interpolated dataset of Fourier layers with mode 12 and width 32 are used. F-FNO: Following the settings in Li et al. (2022a), we train the F-FNO model (Li et al., UNet: Analogous to the setup in Li et al. (2022a), we train a UNet model (Ronneberger A typical training curve can be found in Figure 8. Table 4: The per-epoch runtime (in seconds) of selected models for the hyperelasticity problem. We note that the numbers of trainable parameters for the "Geo-FNO" and "FNO" cases are different from The airfoil dataset is directly taken from Li et al. (2022a), which is an interpolated dataset of The physical parameters used in generating the data are: Y oung's modulus Symmetry is enforced only when the topology characteristic function χ is updated. Besides the resolution-independence property of DAFNO as shown in Figure 3, we further investigate the generalizability of DAFNO in both physical and temporal resolutions with this example. Specifically, the eDAFNO model is trained on a spatial resolution of 64 64 and a time step of 0.02 Our results show that eDAFNO prediction remains independent of the time step employed.